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Abstract Background Over the past decades, the prevalence of obesity among adults has rapidly increased, particularly in socioeconomically deprived urban neighbourhoods. To better understand the complex mechanisms behind this trend, we created a system map exposing the underlying system driving obesity prevalence in socioeconomically deprived urban neighbourhoods over the last three decades in the Netherlands. Methods We conducted Group Model Building (GMB) sessions with a group of thirteen interdisciplinary experts to develop a Causal Loop Diagram (CLD) of the obesogenic system. Using system-based analysis, the underlying system dynamics were interpreted. Results The CLD demonstrates the food environment, physical activity environment, socioeconomic environment and socio-political environment, and their interactions. We identified the following overarching reinforcing dynamics in the obesogenic system in socioeconomically deprived urban neighbourhoods: (1) adverse socioeconomic conditions and an unhealthy food environment reinforced each other, (2) increased social distance between social groups and adverse socioeconomic conditions reinforced each other and (3) increased social distance between institutions and communities and the normalisation of unhealthy behaviours reinforced each other. These deeper system dynamics further reinforced chronic stress, sedentary behaviour, sleeping problems, unhealthy diets and reduced physical activity over time. In turn, these dynamics led to the emergent result of rising obesity prevalence in socioeconomically deprived urban neighbourhoods over the past decades. Conclusions Our study sheds light on the system dynamics leading to neighbourhoods with an unhealthy food environment, challenging socioeconomic conditions, a widening distance between social groups and an infrastructure that discouraged physical activity while promoting sedentary behaviour. Our insights can form the basis for the development of an integrated approach aimed at reshaping the obesogenic system in socioeconomically deprived urban neighbourhoods.

Abstract Background It remains unclear how COVID-19 has disproportionately affected the mental health of different vulnerable groups. This study explores how mental health inequalities changed between 2014 (pre-COVID-19) and 2021 (during COVID-19) in the Netherlands across intersectional social strata defined by interplays of educational attainment, income level, gender, and age. Methods Using 2014 and 2021 self-reported cohort data on health and living conditions of the adult population of Eindhoven and surroundings (N = 1,157), a Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) was applied to explore intersectional inequalities in mental health in 2014, 2021, and in mental health changes (2014–2021). We examined this using the Mental Health Inventory-5 across 53 intersectional social strata based on interplays of education, income, gender, and age in 2014. Results There were substantial differences in mental health trajectories across social strata. Between-stratum mental health inequalities were patterned additively, indicating that inequality patterns along one axis (such as income) tended to be consistent across other axes of comparison. Additive trends revealed that women with a low income were at highest risk of poor mental health in 2014 and 2021, and people over 65 were at highest risk of mental health setbacks over time. Nonsignificant educational inequalities were found in 2014 and 2021. Income inequalities persisted, but slightly decreased in 2021 due to stronger mental health setbacks among those with high incomes. Women experienced persisting disadvantages that slightly flattened over time, and the mental health advantages of older age diminished over time. Conclusions Inequalities in mental health add up for those who experience multiple axes of disadvantage, such as women and those with low incomes, but no disproportionate intersectional interaction effects were found. Effort is needed to ensure that mental health support is accessible for all, especially those with low incomes. Given the especially strong mental health declines among those over 65, responses to future crises need to include measures to protect the mental health of the elderly. Future research should investigate intersectional inequalities along other axes of disadvantage, such as ethnicity, employment and family status.

Abstract Music is a promising (adjunctive) treatment for both acute and chronic pain, reducing the need for pharmacological analgesics and their side effects. Yet, little is known about the effect of different types of music. Hence, we investigated the efficacy of five music genres (Urban, Electronic, Classical, Rock and Pop) on pain tolerance. In this parallel randomized experimental study, we conducted a cold pressor test in healthy volunteers (n = 548). The primary outcome was pain tolerance, measured in seconds. No objective (tolerance time) or subjective (pain intensity and unpleasantness) differences were found among the five genres. Multinomial logistic regression showed that overall genre preference positively influenced pain tolerance. In contrast, the music genres that participants thought would help for pain relief did not. Our study was the first to investigate pain tolerance at genre level and in the context of genre preference without self-selecting music. In conclusion, this study provides evidence that listening to a favored music genre has a significant positive influence on pain tolerance, irrespective of the kind of genre. Our results emphasize the importance of individual music (genre) preference when looking at the analgesic benefits of music. This should be considered when implementing music in the clinical setting.

Although Bourdieu's capital theory emphasized that economic, social, and embodied cultural capital interact to shape health behavior, existing empirical research mainly considered separate associations of the three forms of capital. Our aim was to investigate if and how economic, social, and embodied cultural capital are conditional on each other in their associations with adults' diet and physical activity.Cross-sectional, self-reported data from the 2014 GLOBE survey of 2812 adults aged between 25 and 75 years residing in Eindhoven, the Netherlands were used. Step-wise multiple logistic regression models included economic, social, and embodied cultural capital and adjustment for potential confounders. The models estimated odds ratios of main effects and two-way interactions of the forms of capital with fruit consumption, vegetable consumption, sports participation, and leisure time walking or cycling.In the main effects models, embodied cultural capital was consistently positively associated with all outcomes. Social capital was positively associated with sports participation, fruit consumption, and vegetable consumption, and economic capital was positively associated with sports participation and vegetable consumption. In the two-way interaction models, having specific higher levels of both economic and social capital strengthened their positive association with sports participation. No other combinations of capital were conditional on each other.Economic and social capital were conditional on each other in their association with sports participation, so interventions that provide both economic and social support may be especially effective for increasing this type of physical activity. As its association was strong with all outcomes but not conditional on other forms of capital, embodied cultural capital may operate distinctly from economic and social resources. Policy that takes differences in embodied cultural capital into account or changes to the environment that dampen the importance of embodied cultural resources for health behavior may help improve both diet and physical activity.

Abstract Background Addressing socioeconomic inequalities in early child development (ECD) is key to reducing the intergenerational transmission of health inequalities. Yet, little is known about how socioeconomic inequalities in ECD develop over the course of childhood. Our study aimed to describe how inequalities in ECD by maternal education develop from infancy to middle childhood. Methods We used data from Generation R, a prospective population-based cohort study in The Netherlands. Language skills were measured at ages 1, 1.5, 2, 3, and 4 years, using the Minnesota Child Development Inventory. Socioemotional (i.e. internalizing and externalizing) problems were measured at ages 1.5, 3, 5 and 9 years using the Child Behavior Checklist. We estimated inequalities in language skills and socioemotional problems across the above-mentioned ages, using linear mixed models with standardized scores at each wave. We used maternal education as indicator of socioeconomic position. Results Children of less educated mothers had more reported internalizing (B = 0.72, 95%CI = 0.51;0.95) and externalizing (B = 0.25, 95%CI = 0.10;0.40) problems at age 1.5 years, but better (caregiver reported) language skills at 1 year (B = 0.50, 95%CI = 0.36;0.64) than children of high educated mothers. Inequalities in internalizing and externalizing problems decreased over time. Inequalities in language scores reversed at age 2, and by the time children were 4 years old, children of less educated mothers had substantially lower language skills than children of high educated mothers (B = -0.38, 95%CI = -0.61;-0.15). Conclusions Trajectories of socioeconomic inequality in ECD differ by developmental domain: whereas inequalities in socioemotional development decreased over time, inequalities increased for language development. Children of less educated mothers are at a language disadvantage even before entering primary education, providing further evidence that early interventions are needed.

Common strategies to make official nutrition information more persuasive include highlighting its institutional sources and using simple and direct language. However, such strategies may be counterproductive, as institutions are no longer self-evidently deemed to be legitimate in contemporary societies and such language can be viewed as patronizing. Our preregistered, population-based survey experiment fielded among a high-quality Dutch probability sample in February 2022 (n = 1947) 1) examines whether these dominant strategies hold up when tested against suggestions of psychological reactance and source derogation, and 2) scrutinizes if such responses are stronger among less-educated citizens. Our experiment mirrored real-life examples of health-information campaigns concerning healthy and unhealthy beverages, with data collected on seven outcome measures to discern receptivity toward the information and its sources. We found that just highlighting institutional sources in the information did not lead to it being perceived more negatively. This was also the case when the language used could be perceived as patronizing, with reactance only present for one outcome measure. Moreover, while less-educated citizens were generally less receptive to nutrition information (six of seven outcome measures), versions that could possibly be perceived as patronizing or/and highlighted institutional sources did not make them less receptive systematically. Importantly, therefore, while our results show that the dominant health-communication strategies do not increase receptivity either, their use will probably not have a negative effect on the general public and so do not need to be discarded.

Abstract Background Differences in health care utilization by educational level can contribute to inequalities in health. Understanding health care utilization and health-related quality of life (HRQoL) of educational groups may provide important insights into the presence of these inequalities. Therefore, we assessed characteristics, health care utilization and HRQoL of injury patients by educational level. Method Data for this registry based cohort study were extracted from the Dutch Injury Surveillance System. At 6-month follow-up, a stratified sample of patients (≥25 years) with an unintentional injury reported their health care utilization since discharge and completed the EQ-5-Dimension, 5-Level (EQ-5D-5L) and visual analogue scale (EQ VAS). Logistic regression analyses, adjusting for patient and injury characteristics, were performed to investigate the association between educational level and health care utilization. Descriptive statistics were used to analyse HRQoL scores by educational level, for hospitalized and non-hospitalized patients. Results This study included 2606 patients; 47.9% had a low, 24.4% a middle level, and 27.7% a high level of education. Patients with low education were more often female, were older, had more comorbidities, and lived more often alone compared to patients with high education (p

Abstract Background In the light of urbanization and aging, a crucially relevant policy question is how to shape neighborhoods to foster healthy aging. An important debate is whether older adults should group in neighborhoods, or whether a more mixed neighborhood age composition is more beneficial to health and well-being. We therefore assessed the association between neighborhood age structure and mental health and the mediating role of individual perceptions of neighborhood social factors. Methods We conducted multivariable linear regression models and causal mediation analyses in 1255 older adults of the Dutch Globe study. The neighborhood age structure was measured in 2011 as the homogeneity of the age composition (using the Herfindahl-Hirschman index, range from 0 to 100, a higher score indicating more homogeneity) and the percentage of specific age groups in a neighborhood. Mental health was measured in 2014 by the Mental Health Inventory-5 score (range 0 to 100, a higher score indicating better mental health). Potential mediators were assessed in 2011 and included perceptions of neighborhood social cohesion, feeling at home in a neighborhood, and social participation. Results A more homogeneous age composition (not specified for age) and a higher percentage of children living in a neighborhood were associated with better mental health, the other age categories were not. Social cohesion, feeling at home and social participation did not mediate the associations. Conclusions The neighborhood age composition may be an interesting but currently insufficiently understood entry point for policies to improve older adult’s mental health status.

OBJECTIVE: Using a novel mediation method that presents unbiased results even in the presence of exposure–mediator interactions, this study estimated the extent to which working conditions and health behaviors contribute to educational inequalities in self-rated health in the workforce. METHODS: Respondents of the longitudinal Survey of Health, Ageing, and Retirement in Europe (SHARE) in 16 countries were selected, aged 50–64 years, in paid employment at baseline and with information on education and self-rated health (N=15 028). Education, health behaviors [including body mass index (BMI)] and working conditions were measured at baseline and self-rated health at baseline and two-year follow-up. Causal mediation analysis with inverse odds weighting was used to estimate the total effect of education on self-rated health, decomposed into a natural direct effect (NDE) and natural indirect effect (NIE). RESULTS: Lower educated workers were more likely to perceive their health as poor than higher educated workers [relative risk (RR) 1.48, 95% confidence interval (CI) 1.37–1.60]. They were also more likely to have unfavorable working conditions and unhealthy behaviors, except for alcohol consumption. When all working conditions were included, the remaining NDE was RR 1.30 (95% CI 1.15–1.44). When BMI and health behaviors were included, the remaining NDE was RR 1.40 (95% CI 1.27–1.54). Working conditions explained 38% and health behaviors and BMI explained 16% of educational inequalities in health. Including all mediators explained 64% of educational inequalities in self-rated health. CONCLUSIONS: Working conditions and health behaviors explain over half of the educational inequalities in self-rated health. To reduce health inequalities, improving working conditions seems to be more important than introducing health promotion programs in the workforce.

In recent years, mediation analysis has become a popular means to identify and quantify pathways linking an exposure to an outcome, thereby elucidating how a particular exposure contributes to the occurrence of a specific outcome. When a mediator is a modifiable risk factor, this opens up new opportunities for interventions to block (part) of the exposure`s effect on the outcome. Recent examples in Scandinavian Journal of Work Environment Health have addressed the mediating effect of wellbeing on the association between type of office and job satisfaction (1) and examined whether workplace social capital contributes to the association between organizational changes and employee exit from work (2). Mediation analysis requires a specific study design ensuring that the temporal sequence of exposure, mediator, and outcome supports the argument for causation. A good illustration presented by Halonen and colleagues (3) used the Swedish Longitudinal Occupational Survey of Health (SLOSH) with biannual waves to investigate whether depressive symptoms mediated the association between effort–reward imbalance and subsequent neck-shoulder pain. The longitudinal design allowed for a mediation analysis whereby exposure was measured two years before the mediator, and the mediator was assessed two years before the outcome of interest. When exposure and mediator are self-reported, without assurance that the mediator occurred after the exposure (eg, years of education in school preceding exposure to working conditions), mediation analysis is prone to reversed causality (ie, not the exposure causing the mediator but the mediator causing the exposure) and has little to ­offer in terms of a better understanding of the mechanism how exposure is linked to the outcome. A linked issue is the stability of the mediator over time. Some authors have argued that mediation analysis is bound to fail in studies with a time window of measurements that does not sufficiently capture the fluctuations of the potential mediator over time (4). Recent debates on mediation analysis have specifically addressed the methodology and assumptions that allow for a causal interpretation. The traditional approach to mediation analysis in health research has largely relied on the influential 1986 paper of Baron & Kenny (5). In this approach, mediation is assessed by estimating the effect of the exposure on the mediator and the effect of the mediator on the outcome (adjusting for the exposure), and multiplying the two estimates to derive the indirect or mediated effect (the "product-of-coefficients" ­approach). Alternatively, the effect of the exposure on the outcome is estimated with and without adjustment for the mediator, and the difference between the two estimates is used to quantify the mediated effect (the "difference-of-coefficients" approach). What has been largely overlooked, however, is the fact that these approaches are only valid in linear models and rely on the assumption that there is no interaction between exposure and mediator on the outcome (6). Moreover, because the importance of controlling for mediator-outcome confounders was never mentioned in Baron & Kenny, many studies have neglected to account for potential confounding factors of the mediator-outcome relationship. An eloquent illustration of the problems of the classical mediation method and the risk of severe bias in the mediated effect is presented by Pearce & Vandenbroucke (7). In response to the limitations of traditional mediation methods, a new mediation methodology is rapidly developing and often referred to as "causal mediation analysis" (6, 8). This methodology has derived counter­factual definitions of direct and indirect effects, which are independent of any model, and – under explicitly stated assumptions – allow for a causal interpretation of the estimated effects (9). These definitions enable greater flexibility and provide researchers with the tools to scrutinize the assumptions underlying mediation analysis better. The causal inference approach to mediation has given rise to two different types of effects: (i) natural direct and indirect effects, and (ii) controlled direct effects. Natural direct and indirect effects by definition always sum up to the total effect, even in the presence of exposure–mediator interaction. Intuitively, the natural direct effect captures the effect of the exposure on the outcome that is not due to its effect on the mediator, whereas the natural indirect effect captures the effect of the exposure on the outcome that is due to its effect on the mediator (8). Controlled direct effects, on the other hand, quantify the effect of the exposure on the outcome if the mediator was fixed at a specific value uniformly in the population. Consequently, whereas a total effect can always be decomposed into a natural direct and indirect effect, controlled direct effects are estimated for every level of the mediator (which may differ substantially depending on the magnitude of the interaction effect between exposure and mediator). Decomposition of a total effect of an exposure on an outcome into natural direct and indirect effects requires that there is no unmeasured confounding of the (i) exposure–outcome, (ii) exposure–mediator, and (iii) ­mediator–outcome relationships. Furthermore, a critical assumption is (iv) that there are no measured or unmeasured mediator–outcome confounders that are themselves affected by the exposure (6). The first three assumptions are depicted in figure 1 (see PDF): if C1, C2 and C3 include all relevant confounders of the three pathways under study (A→Y, A→M, and M→Y), the first three assumptions are met. The critical fourth assumption that the mediator–outcome association is not confounded by any variable that is itself affected by the exposure is depicted in figure 2 (see PDF). Here (L) is both a confounder of the M→Y path and a mediator on the A→Y path. An example would be the association between shift work (A) and diabetes mellitus (Y), as reported by Tucker and colleagues (10). Lack of leisure-time physical activity may be considered a mediator (M). Whereas controlling for confounders (C) (eg, age and sex) is straightforward, controlling for overweight (L) may be problematic. The issue here is that overweight may be a confounder of the association between leisure-time physical activity and diabetes mellitus (individuals with overweight might find it difficult to be active in their leisure), and overweight may also be on the causal pathway from shift work to diabetes mellitus (eg, by an effect of shift work on dietary patterns or physiological processes). In the presence of such exposure-induced mediator–outcome confounders, the researcher is stuck between bad choices: not adjusting for (L) will bias the estimated effect of the mediator; adjusting for (L) will bias the estimated effect of the exposure. Hence, researchers must convince themselves that such confounding is not present. When potential confounding is a serious possibility, natural direct and indirect effects cannot be identified, regardless of whether (L) is measured or not. In such a situation, new methods are required that allow for effect decomposition in the presence of exposure-induced mediator–outcome confounders by using so-called interventional (in)direct effects, which consider potential interventions on a population level (11, 12). An alternative approach in mediation analysis is to quantify controlled direct effects that only rely on assumptions (i) and (iii): no uncontrolled exposure–outcome confounding and no uncontrolled mediator–outcome confounding. Moreover, some have argued that controlled direct effects are much more policy-relevant because they estimate the proportion of the total effect of the exposure on the outcome that could be eliminated by a specific intervention on the mediator (13, 14). Although controlled direct effects can still be estimated in the presence of exposure-induced mediator–outcome confounders, this does require more sophisticated methods, such as marginal structural models (15, 16) or the g-formula (17). Albeit still infrequently used, these approaches offer an important new toolbox for health research and are often easily implemented in standard statistical software (18). To conclude, mediation analysis has gained a lot of attention in recent years and is rapidly developing with regard to approach and methods. We invite researchers to move away from traditional mediation analysis and apply the newer mediation methods in their studies. First, a critical stance is required to evaluate whether the study design and data collected permit a meaningful mediation analysis. Second, studies should explicitly address the crucial assumptions that are fundamental for a causal interpretation of mediation. Third, sensitivity analysis is helpful to assess the robustness of the results to potential violations of the underlying assumptions. References 1. Otterbring T, Pareigis J, Wästlund E, Makrygiannis A, Lindström A. The relationship between office type and job satisfaction: Testing a multiple mediation model through ease of interaction and well-being. Scand J Work Environ Health. 2018;44(3):330-34. https://doi.org/10.5271/sjweh.3707 2. Jensen JH, Flachs EM, Skakon J, Rod NH, Bonde JP. Longitudinal associations between organizational change, work-unit social capital, and employee exit from the work unit among public healthcare workers: a mediation analysis. Scand J Work Environ Health. 2019;45:53-62. https://doi.org/10.5271/sjweh.3766 3. Halonen JI, Lallukka T, Virtanen M, Rod NH, Magnusson Hanson LL. Bi-directional relation between effort-reward imbalance and risk of neck-shoulder pain: assessment of mediation through depressive symptoms using occupational longitudinal data. Scand J Work Environ Health. 2019;45:126-33. https://doi.org/10.5271/sjweh.3768 4. Rod NH, Lange T. Re: Current limits for understanding the underlying causal structures in observational epidemiology: reflections on refined mediation models and crude data. Epidemiology 2017;28:e60-61. https://doi.org/10.1097/EDE.0000000000000708 5. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173-82. https://doi.org/10.1037/0022-3514.51.6.1173 6. Valeri L, VanderWeele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods 2013;18:137-50. https://doi.org/10.1037/a0031034 7. Pearce N, Vandenbroucke JP. Causation, mediation and explanation. Int J Epidemiol 2016;45:1915-22. https://doi.org/10.1093/ije/dyw281 8. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol 2013;42:1511-9. https://doi.org/10.1093/ije/dyt127 9. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology 1992;3:143-55. https://doi.org/10.1097/00001648-199203000-00013 10. Tucker P, Härmä M, Ojajärvi A, Kivimäki M, Leineweber C, Oksanen T, et al. Associations between shift work and use of prescribed medications for the treatment of hypertension, diabetes, and dyslipidemia: a prospective cohort study. Scand J Work Environ Health. 2019;45:465-74. https://doi.org/10.5271/sjweh.3813 11. Moreno-Betancur M, Carlin JB. Understanding Interventional Effects: A More Natural Approach to Mediation Analysis? Epidemiology 2018;29:614-7. https://doi.org/10.1097/EDE.0000000000000866 12. Vanderweele TJ, Vansteelandt S, Robins JM. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology 2014;25:300-6. https://doi.org/10.1097/EDE.0000000000000034 13. VanderWeele TJ. Policy-relevant proportions for direct effects. Epidemiology 2013;24:175-6. https://doi.org/10.1097/EDE.0b013e3182781410 14. Naimi AI, Kaufman JS, MacLehose RF. Mediation misgivings: ambiguous clinical and public health interpretations of natural direct and indirect effects. Int J Epidemiol 2014;43:1656-61. https://doi.org/10.1093/ije/dyu107 15. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11:550-60. https://doi.org/10.1097/00001648-200009000-00011 16. Nandi A, Glymour MM, Kawachi I, VanderWeele T. Using marginal structural models to estimate the direct effect of adverse childhood social conditions on onset of heart disease, diabetes, and stroke. Epidemiology 2012;23:223-32. https://doi.org/10.1097/EDE.0b013e31824570bd 17. Vangen-Lonne AM, Ueda P, Gulayin P, Wilsgaard T, Mathiesen EB, Danaei G. Hypothetical interventions to prevent stroke: an application of the parametric g-formula to a healthy middle-aged population. Eur J Epidemiol 2018;33:557-66. https://doi.org/10.1007/s10654-017-0344-x 18. Hernán MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, 2020.

Objectives This study aims to investigate the association of neighbourhood socioeconomic status (SES) and social cohesion (SC) within the neighbourhood with mental health service use in children, independent of individual-level characteristics and mental health problems.Design, setting and participants A longitudinal analysis was done using data from the Generation R Study, a prospective, population-based cohort of children born in Rotterdam, the Netherlands. These data were linked to the Neighbourhood Profile, containing registry and survey data on residents of Rotterdam. Data of 3403 children (mean age: 13.6 years, SD: 0.4) were used to study the associations between neighbourhood SES, SC (SC belonging and SC relations) and mental health service use, adjusted for mental health problems and sociodemographic characteristics.Outcome measures Mental health service use was reported by the accompanying parent at the research centre using the question: ‘Did your child visit a psychologist or psychiatrist between 9 and 13 years old?’.Results Mental health services were used by 524 (15.4%) children between ages 9 and 13 years. No significant differences in mental health service use between neighbourhoods were identified (median OR: 1.07 (p=0.50)). The neighbourhood social characteristics were associated with mental health service use, but only when adjusted for each other. Children living in neighbourhoods with a low SES (OR 0.57 (95% CI 0.32 to 1.00)) or high SC belonging (OR 0.79 (95% CI 0.64 to 0.96)) were less likely to use services compared with children in a high SES or low SC belonging neighbourhood. SC relations was not associated with mental health service use.Conclusions Our findings indicate that children living in high SES neighbourhoods or in neighbourhoods where people feel less sense of belonging are more likely to use mental health services. As these associations were only present when studied jointly, more research is warranted on the complex associations of neighbourhood factors with children’s mental health service use.

Unhealthy diets are a major threat to population health and are especially prevalent among those with a low socioeconomic status (SES). Health promotion initiatives often rely on nutrition information interventions (NIIs), but are usually less effective among adults with a low SES than in their high-SES counterparts. Explanations for this lower effectiveness are set out in extant studies. These have been conducted across a wide range of disciplines and subject fields and using a variety of methodological approaches. We have therefore conducted a scoping review to identify and synthesise the following: (1) explanations suggested in studies carried out in high-income countries for why NIIs are (in)effective among adults with a low SES and (2) whether these suggested explanations were studied empirically. Eight databases were searched for relevant studies published since 2009 across various disciplines. This identified 4951 papers, 27 of which were included in our review after screening. Only fifteen of these proposed an explanation for the (in)effectiveness of NIIs among adults with a low SES. The following four main themes were uncovered: health literacy, economic resources, social resources and convenience. Ten studies tested their explanations empirically, but the results were inconsistent. The reasons why NIIs are (in)effective among low-SES adults are therefore still largely unclear. Also, current literature predominantly relies on individualistic explanations, most notably focusing on psychological and economic attributes. Consequently, if the effectiveness of NIIs among low-SES populations is to be improved, future studies should examine a wider range of explanations and test them systematically and empirically.

Abstract Background Social capital is an important determinant of health, but how specific sub-dimensions of social capital affect health and health-related behaviors is still unknown. To better understand its role for health inequalities, it is important to distinguish between bonding social capital (connections between homogenous network members; e.g. similar educational level) and bridging social capital (connections between heterogeneous network members). In this study, we test the hypotheses that, 1) among low educational groups, bridging social capital is positively associated with health-behavior, and negatively associated with overweight and obesity, and 2) among high educational groups, bridging social capital is negatively associated with health-behavior, and positively with overweight and obesity. Methods Cross-sectional data on educational level, health-behavior, overweight and obesity from participants (25–75 years; Eindhoven, the Netherlands) of the 2014-survey of the GLOBE study were used (N = 2702). Social capital (“How many of your close friends have the same educational level as you have?”) was dichotomized as: bridging (‘about half’, ‘some’, or ‘none of my friends’), or bonding (‘all’ or ‘most of my friends’). Logistic regression models were used to study whether bridging social capital was related to health-related behaviors (e.g. smoking, food intake, physical activity), overweight and obesity, and whether these associations differed between low and high educational groups. Results Among low educated, having bridging social capital (i.e. friends with a higher educational level) reduced the likelihood to report overweight (OR 0.73, 95% CI 0.52–1.03) and obesity (OR 0.58, 95% CI 0.38–0.88), compared to low educated with bonding social capital. In contrast, among high educated, having bridging social capital (i.e. friends with a lower educational level) increased the likelihood to report daily smoking (OR 2.11, 95% CI 1.37–3.27), no leisure time cycling (OR 1.55, 95% CI 1.17–2.04), not meeting recommendations for vegetable intake (OR 2.09, 95% CI 1.50–2.91), and high meat intake (OR 1.39, 95% CI 1.05–1.83), compared to high educated with bonding social capital. Conclusions Bridging social capital had differential relations with health-behavior among low and high educational groups. Policies aimed at reducing segregation between educational groups may reduce inequalities in overweight, obesity and unhealthy behaviors.

Abstract Background The importance of culture for food consumption is widely acknowledged, as well as the fact that culture-based resources (“cultural capital”) differ between educational groups. Since current explanations for educational inequalities in healthy and unhealthy food consumption (e.g. economic capital, social capital) are unable to fully explain this gradient, we aim to investigate a new explanation for educational inequalities in healthy food consumption, i.e. the role of cultural capital. Methods Data were obtained cross-sectionally by a postal survey among participants of the GLOBE study in the Netherlands in 2011 (N = 2953; response 67.1%). The survey measured respondents’ highest attained educational level, food-related cultural capital (institutionalised, objectivised and incorporated cultural capital), economic capital (e.g. home ownership, financial strain), social capital (e.g. social support, health-related social leverage, interpersonal relationships), and frequency of consumption of healthy and unhealthy food products. Two general outcomes (overall healthy food consumption, and overall unhealthy food consumption), and seven specific food consumption outcomes were constructed, and prevalence ratios (PR) were estimated in Poisson regression models with robust variance. Results Cultural capital was significantly associated with all food outcomes, also when social and economic capital were taken into account. Those with low levels of cultural capital were more likely to have a lower overall healthy food consumption (PR 1.35, 95% CI 1.22–1.49), a lower consumption of whole wheat bread (PR 1.21, 95% CI 1.05–1.38), vegetables (PR 1.55, 95% CI 1.40–1.71), and meat-substitutes and fish (PR 1.74, 95% CI 1.53–1.97), and a higher consumption of fried food (PR 1.59, 95% CI 1.31–1.93). Social capital was positively associated with overall healthy food consumption, whole wheat bread consumption, and the consumption of fish and meat-substitutes, and economic capital with none of the outcomes. The PR of the lowest educational group to have a low overall healthy food consumption decreased from 1.48 (95% CI 1.28–1.73) to 1.22 (95% CI 1.04–1.43) when cultural, social and economic capital were taken into account. Conclusions Cultural capital contributed to the explanation of educational inequalities in food consumption in The Netherlands, over and above economic and social capital. The socialisation processes through which cultural capital is acquired could offer new entry-points for the promotion of healthy food consumption among low educational groups.

Abstract Background Material and behavioural factors play an important role in explaining educational inequalities in mortality, but gender differences in these contributions have received little attention thus far. We examined the contribution of a range of possible mediators to relative educational inequalities in mortality for men and women separately. Methods Baseline data (1991) of men and women aged 25 to 74 years participating in the prospective Dutch GLOBE study were linked to almost 23 years of mortality follow-up from Dutch registry data (6099 men and 6935 women). Cox proportional hazard models were used to calculate hazard ratios with 95% confidence intervals, and to investigate the contribution of material (financial difficulties, housing tenure, health insurance), employment-related (type of employment, occupational class of the breadwinner), behavioural (alcohol consumption, smoking, leisure and sports physical activity, body mass index) and family-related factors (marital status, living arrangement, number of children) to educational inequalities in all-cause and cause-specific mortality, i.e. mortality from cancer, cardiovascular disease, other diseases and external causes. Results Educational gradients in mortality were found for both men and women. All factors together explained 62% of educational inequalities in mortality for lowest educated men, and 71% for lowest educated women. Yet, type of employment contributed substantially more to the explanation of educational inequalities in all-cause mortality for men (29%) than for women (− 7%), whereas the breadwinner’s occupational class contributed more for women (41%) than for men (7%). Material factors and employment-related factors contributed more to inequalities in mortality from cardiovascular disease for men than for women, but they explained more of the inequalities in cancer mortality for women than for men. Conclusions Gender differences in the contribution of employment-related factors to the explanation of educational inequalities in all-cause mortality were found, but not of material, behavioural or family-related factors. A full understanding of educational inequalities in mortality benefits from a gender perspective, particularly when considering employment-related factors.

Abstract Background The key mechanisms underlying socioeconomic inequalities in dietary intake are still poorly understood, hampering the development of interventions. An important, but sparsely mentioned mechanism is that of ‘social distinction’, whereby those in a higher socioeconomic position adopt dietary patterns by which they can distinguish themselves from lower socioeconomic groups. We investigated the importance of distinction as a mechanism, by testing the socioeconomic gradient in the consumption of so-called ‘superfoods’ and the contribution of a well-established indicator of distinction, cultural participation. Methods Data from participants (25–75 years) of the 2014 survey of the Dutch population-based GLOBE study were used (N = 2812). Multivariable regression models were used to analyse the association between education, income and cultural participation (e.g. visits to museums, opera, theatre, concerts) and the consumption of superfoods (spelt, quinoa and goji berries, chia seeds or wheatgrass). Results The consumption of superfoods is far more prevalent among higher socioeconomic groups. Adjusting for cultural participation strongly attenuated the educational and income gradient in superfoods consumption, whereas cultural participation remained strongly associated with superfoods consumption. Those in the highest quintile of cultural participation reported the highest consumption of spelt products (OR = 2.97, 95% CI = 2.10;4.18), quinoa (OR = 3.50, 95% CI = 2.12;5.79) and goji berries, chia seeds or wheatgrass (OR = 2.69, 95% CI = 1.73;4.17). Conclusions The associations between socioeconomic position and the consumption of ‘superfoods’ seem to be partially driven by a process of social distinction. These findings suggest that distinction may be an important, but currently neglected mechanism in generating socioeconomic inequalities in dietary intake. It deserves a more prominent role in interventions to reduce these inequalities.

Abstract Background This study examined whether characteristics of the residential built environment (i.e. population density, level of mixed land use, connectivity, accessibility of facilities, accessibility of green) contributed to educational inequalities in walking and cycling among adults. Methods Data from participants (32–82 years) of the 2011 survey of the Dutch population-based GLOBE study were used (N = 2375). Highest attained educational level (independent variable) and walking for transport, cycling for transport, walking in leisure time and cycling in leisure time (dependent variables) were self-reported in the survey. GIS-systems were used to obtain spatial data on residential built environment characteristics. A four-step mediation-based analysis with log-linear regression models was used to examine to contribution of the residential built environment to educational inequalities in walking and cycling. Results As compared to the lowest educational group, the highest educational group was more likely to cycle for transport (RR 1.13, 95% CI 1.04–1.23), walk in leisure time (RR 1.12, 95% CI 1.04–1.21), and cycle in leisure time (RR 1.12, 95% CI 1.03–1.22). Objective built environment characteristics were related to these outcomes, but contributed minimally to educational inequalities in walking and cycling. On the other hand, compared to the lowest educational group, the highest educational group was less likely to walk for transport (RR 0.91, 95% CI 0.82–1.01), which could partly be attributed to differences in the built environment. Conclusion This study found that objective built environment characteristics contributed minimally to educational inequalities in walking and cycling in the Netherlands.